Metformin shows anti‐inflammatory effects in murine macrophages through Dicer/microribonucleic acid‐34a‐5p and microribonucleic acid‐125b‐5p
Bibliographic record
Abstract
AIMS/INTRODUCTION: Metformin, a widely prescribed antidiabetic agent, has been shown to exhibit anti-inflammatory effects in obese and type 2 diabetes patients, but the mechanism is not well elucidated. Microribonucleic acids (miRNAs) are a group of small non-coding ribonucleic acids that participate in many biological and pathological processes. The aim of the present study was to investigate whether Dicer, a key miRNA biogenesis enzyme, and miRNAs in macrophages are implicated in the anti-inflammatory effects of metformin. MATERIALS AND METHODS: Enzyme-linked immunosorbent assay and reverse transcription quantitative polymerase chain reaction were carried out to verify the anti-inflammatory effects of metformin. miRNA microarray was applied to detect the expression profile of miRNA. Western-blotting, enzyme-linked immunosorbent assay and reverse transcription quantitative polymerase chain reaction were used to examine the role Dicer and miRNAs play in the anti-inflammatory effects of metformin. RESULTS: In parallel with the suppression of interleukin-6 and tumor necrosis factor-α production in resting and lipopolysaccharide-stimulated macrophages, metformin could induce an increase in Dicer and most miRNAs. When Dicer was knocked down, the anti-inflammatory effects of metformin were significantly attenuated. Additionally, the upregulation of miRNA (miR)-34a-5p and miR-125b-5p by metformin were also blunted in Dicer knockdown macrophages. Furthermore, inhibition of miR-34a-5p and miR-125b-5p could impair the suppressive action of metformin on pro-inflammatory factors production, whereas overexpression of the two miRNAs mimicked the anti-inflammatory effects of metformin. CONCLUSIONS: Metformin might show anti-inflammatory effects in macrophages through the induction of Dicer and the subsequent upregulation of miR-34a-5p and miR-125b-5p.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".